Antimicrobial Activity Characterization in a Heterogeneous Group of Compounds

García-Domènech, Ramón; Julián-Ortiz, Jesús Vicente de

doi:10.1021/ci9702454

Cited by 71 publications

(56 citation statements)

References 27 publications

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“…The latest series of QSAR works report effective separation of bioactive substances from the non-active chemicals by applying the methods of Support Vector Machines (SVM) [17,18], probability-based classification [19], the Artificial Neural Networks (ANN) [20][21][22] and the Bayesian Neural Networks (BNN) [23,24] among others. Several groups used datasets of antibacterial compounds to build the binary classifiers of general antibacterial activity (antibiotic-likeness models) utilizing the ANN algorithm [25][26][27], linear discriminant analysis (LDA) [28,29], binary logistic regression [29] or k-means cluster method [30]. Thus, in the study [31] the LDA has been used to relate anti-malarial activity of a series of chemical compounds to molecular connectivity QSAR indices.…”

Section: Resultsmentioning

confidence: 99%

Inductive QSAR Descriptors. Distinguishing Compounds with Antibacterial Activity by Artificial Neural Networks

Cherkasov

2005

IJMS

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On the basis of the previous models of inductive and steric effects, 'inductive' electronegativity and molecular capacitance, a range of new 'inductive' QSAR descriptors has been derived. These molecular parameters are easily accessible from electronegativities and covalent radii of the constituent atoms and interatomic distances and can reflect a variety of aspects of intra-and intermolecular interactions. Using 34 'inductive' QSAR descriptors alone we have been able to achieve 93% correct separation of compounds with-and without antibacterial activity (in the set of 657). The elaborated QSAR model based on the Artificial Neural Networks approach has been extensively validated and has confidently assigned antibacterial character to a number of trial antibiotics from the literature.

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Section: Resultsmentioning

confidence: 99%

Inductive QSAR Descriptors. Distinguishing Compounds with Antibacterial Activity by Artificial Neural Networks

Cherkasov

2005

IJMS

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“…These QSAR approaches process a variety of structure-dependent descriptors with machine learning and statistical techniques such as Artificial Neural Networks [1][2][3], Linear Discriminant Analysis, [4][5][6] Binary Logistic Regression [5], Principal Component Analysis and k-means Cluster method [7]. In some cases the results allowed the authors to introduce novel anti-infective leads, however, all of the reported QSAR solutions have been built upon already well -studied classes of traditional antibiotics.…”

Section: Qsar Models For Antibiotic Activitymentioning

confidence: 99%

Application of ‘Inductive’ QSAR Descriptors for Quantification of Antibacterial Activity of Cationic Polypeptides

Cherkasov

Jankovic²

2004

Molecules

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Abstract:On the basis of the inductive QSAR descriptors we have created a neural network-based solution enabling quantification of antibacterial activity in the series of 101 synthetic cationic polypeptides (CAMEL-s). The developed QSAR model allowed 80% correct categorical classification of antibacterial potencies of the CAMEL-s both in the training and the validation sets. The accuracy of the activity predictions demonstrates that a narrow set of 3D sensitive 'inductive' descriptors can adequately describe the aspects of intra-and intermolecular interactions that are relevant for antibacterial activity of the cationic polypeptides. The developed approach can be further expanded for the larger sets of biologically active peptides and can serve as a useful quantitative tool for rational antibiotic design and discovery.

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“…In this case the value for the Matthew s indicates an appropriate agreement between the assignments and the predictions from the model established [70]. Finally, the high value of 1 ¼ 9.0 shows that the model is not overfitted by an excess of parameters; this parameter is expected to be > 4 for linear models [69].…”

Section: Resultsmentioning

confidence: 82%

“…In addition, we controlled the Matthew s coefficient (C ¼ 0.698) [68] and the cases/adjustable parameters ratio (1 ¼ 9.0) taking into account the smallest group into the classification [69].…”

Section: Resultsmentioning

confidence: 99%

Theoretical Prediction of Antiproliferative Activity against Murine Leukemia Tumor Cell Line (L1210). 3D‐Morse Descriptor and its Application in Computational Chemistry

et al. 2009

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Cancer is among the top ten causes of death in the world but in spite of the efforts of the pharmaceutical companies and many governmental organizations, new and more effective drugs are urgently needed. Computer-assisted studies have been widely used to predict anticancer activity taking into account different molecular descriptors, statistical techniques, cell lines, and datasets of congeneric and non-congeneric compounds. This paper describes a QSAR study and the successful application of 3D-MoRSE descriptor for developing Linear Discriminant Analysis (LDA) to predict the anticancer potential of a diverse set of indolocarbazoles derivatives. Despite the structural complexity of this sort of compounds the used variables are able to identify the most remarkable features like the incidence of polarizability of the substituents and the interatomic distance in the 7-azaindole moiety in the antiproliferative activity. A comparison with other approaches such as the Getaway, Randić molecular profile, Geometrical, RDF descriptors, was carried out showing the model with 3D-MoRSE descriptor resulting in the best accuracy and predictive capability. An LDA-based desirability analysis was conducted to select the levels of the predictor variables, in other words, the values of the independent variables which should generate more desirable anticancer chemicals, i.e., with higher posterior probability to be classified cytotoxic.

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Antimicrobial Activity Characterization in a Heterogeneous Group of Compounds

Cited by 71 publications

References 27 publications

Inductive QSAR Descriptors. Distinguishing Compounds with Antibacterial Activity by Artificial Neural Networks

Inductive QSAR Descriptors. Distinguishing Compounds with Antibacterial Activity by Artificial Neural Networks

Application of ‘Inductive’ QSAR Descriptors for Quantification of Antibacterial Activity of Cationic Polypeptides

Theoretical Prediction of Antiproliferative Activity against Murine Leukemia Tumor Cell Line (L1210). 3D‐Morse Descriptor and its Application in Computational Chemistry

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